A. Field of the Invention
The present invention relates to robot networks, and, more particularly, to methods and systems for reconfiguring robots of a network.
B. Description of the Related Art
In recent years, robot networks have become more popular and their assigned tasks have become more sophisticated. Typically, robot networks include a plurality of robots that operate according to an on-board control logic. The control logic acts as the "brains" of each robot and defines the action of the robot in response to a sensed input from the environment. For instance, the control logic may define how the robot processes an input data signal or moves in response to a sensed environmental condition. Thus, the performance of the overall robot network is necessarily a function of the accuracy of the control logic.
The article "Issues in Evolutionary Robotics," Harvey et al., Proceedings of the Second International Conference on Simulation of Adaptive Behavior, (1993), describes a method for reconfiguring the control logic of each robot in a network. Under this approach, each robot is initially downloaded with a different control logic. Thus, the robots of the robot network will each perform the assigned task with a varying degree of success. To improve the performance of those robots that are less successful, a new control logic is determined "off-line." In particular, genetic programming techniques are used to reconfigure the control logic of the more successful robots to produce a new control logic for the less successful robots. These genetic programming techniques, which include mutation and cross-over techniques, are well known in the art and are used to produce an evolved control logic by reconfiguring the control logic of the more successful robots. After this new control logic is downloaded onto the less successful robots, the robots are then placed back into operation to accomplish an actual task.
A problem with the above approach, however, is that the robots cannot reconfigure their control logic dynamically while performing their assigned task. The new control logic must be determined "off-line." Nor does this approach allow the robots to reconfigure their control logic using data detected while performing an actual task at hand. In the above approach, the new control logic is determined "off-line" using a predefined set of environmental conditions.
Other robot networks include robots that share information with one another. Thus, data detected by one robot may be shared with the other robots of the network to help those other robots achieve a commonly assigned task. However, these approaches also fail to allow the robots to dynamically reconfigure their control logic. In other words, these networks merely allow robots to share information, but do not allow the robots to change how they process that information.
Therefore, there is a need for a robot network that allows the individual robots to optimally reconfigure their on-board control logic on a real-time basis.